Why Recursion is getting love as an AI and drug discovery top pick
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Seeking Alpha’s Investing Experts Podcast recently discussed the biotech sector – an area where investing is traditionally considered risky – and the impact of artificial intelligence (AI) on drug discovery.
Kirk Spano, investing group leader of Margin of Safety Investing, and Bhavneesh Sharma, investing group leader of Vasuda Healthcare Analytics, spoke on the podcast on Sunday.
The U.S. biotech sector is known for its volatility from an investing perspective. There are thousands of tiny biopharmaceutical firms jostling for space, and the journey from an in-development investigational treatment to a commercialized, approved therapy can take several years.
Biotechs are known for wild swings in their stock prices in reaction to several catalysts such as trial results and regulatory action by the U.S. Food and Drug Administration (FDA).
“The biotech sector is considered very risky. And traditionally, it has been considered risky. Now, one of the reasons is that most investors or traders they try to play the catalyst,” Bhavneesh Sharma said on the podcast.
“What it means is that they would just buy a biotech or pharma stock about three to six months before a catalyst, which is like a FDA decision date or upcoming data release, like Phase 2 or Phase 3 data,” Sharma added.
A Contrarian Buying Opportunity
The analyst, who is also a doctor, believes that predicting FDA decisions or trial results is “never totally possible” for an expert in the biotech sector.
Additionally, Sharma thinks “it is almost impossible” to predict developments on the manufacturing front. The FDA is very strict about manufacturing processes, and often rejects treatment applications on those grounds through so-called complete response letters (CRLs).
“I have found that these kind of manufacturing-related CRLs…are a very good contrarian buying opportunity because these manufacturing problems are very easy to fix. It’s not like the company is not being asked to conduct another trial, which takes like two years,” Sharma said.
So there might be just some small manufacturing issues or drug labeling issues and usually these are fixable within six months. Buying the dip up, if you don’t have a position in that, it is a very good idea because often the company’s stock will fall like 50%, 75% on the CRL. And then if you buy them, then it might like double over the next one year. That is only for manufacturing-related CRLs,” Sharma added.
AI and Drug Discovery
The Vasuda Healthcare Analytics investing group leader believes that one of the biggest breakthroughs AI has brought to drug discovery is the fact that one can now simulate proteins and their structures.
The process of drug discovery involves identifying therapeutics targets in the body that reside on proteins. Earlier, this process was only possible through pre-clinical studies on animals followed by clinical trials in humans.
“(Trials) was the only way to really check that it was like shooting blindly that okay you have a target, there is some background to that, that this target may be effective, but really, the drug has to go there and completely bind and block that target either block or either stimulate that target for it to be very effective,” Sharma said.
“And the only way to do that was to conduct these trials and then if it fails and then do the other trial and sometimes they will succeed in the animal trials, but they will fail in the human trials,” Sharma added.
But now with AI, the journey can become much simpler.
“Using the AI really what you can do is that you can simulate, you can like what you can do is that you have, let’s say, protein and you have a three-dimensional structure of that and you can really look – you can really simulate that which of these targets are going to be most beneficial,” according to Sharma.
The analyst added that AI has now helped reduce the drug discovery process to hours and days from months and years.
Sharma’s Number One Pick
The investing group leader believes that in the AI and drug discovery space, companies that have their own software and databases in which they have already modeled billions of targets will have an edge.
Sharma highlighted Salt Lake City, Utah-based Recursion (NASDAQ:RXRX) as his top pick in the space.
“This is a company which has their own software called Recursion OS, and they have already modeled billions of potential targets in the proteomics and genomics,” Sharma said.
Notably, chip giant Nvidia (NVDA) invested $50M in Recursion (RXRX) in July 2023 to help it develop foundation models.
Click here for the full podcast.
Here are some biotech-focused exchange-traded funds of interest: (IBB), (XBI), (FBT), (BBH), (PBE), (IDNA), (GNOM), (BIB), and (HELX).